Relevant bibliographies by topics / Resource allocating

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Resource allocating'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Resource allocating"

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Edavalath, Sheena, and Manikandasaran S. Sundaram. "MARCR: Method of allocating resources based on cost of the resources in a heterogeneous cloud environment." Scientific Temper 14, no. 03 (2023): 576–81. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.03.

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The cloud is an intelligent technology that provides requested services to users. It offers unlimited services for the users. Many small and medium-scale industries are startup their businesses to the next level using cloud computing. The services have been provided to the users by allocating the requested resources. Allocating resources without waste and with the finest allocation is a critical task in the cloud. This paper proposes a method for allocating resources using the cost of the resource. Resource allocation follows a priority system when allocating resources. The proposed method gives priority to low-cost resources. The cost denotes the service cost of the resource. The requested resource is assigned to the user by the CSP, who provides the specific resource at a low cost. This proposed method suggests a UHRAM for collecting and allocating the resources from the different CSPs. UHRAM is a centralized hub for delivering requested resources to users, and it maintains a repository of details about the resources from all CSPs in the heterogeneous cloud. The proposed method is implemented with the user’s data. The results from the comparison show that the proposed cost-based resource allocation method is more efficient than existing methods.
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Edavalath, Sheena, and Manikandasaran S. Sundaram. "Cost-based resource allocation method for efficient allocation of resources in a heterogeneous cloud environment." Scientific Temper 14, no. 04 (2023): 1339–44. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.41.

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Cloud computing is appealing due to features like adaptability, portability, utility service and on-demand service. Cloud resource providers are a source of computing, and each provider delivers different types of resources. In an active cloud environment, timely resource allocation is more important. In order to increase the effectiveness and user-friendliness of resource allocation in the heterogeneous cloud, the paper suggests an efficient cost-based resource allocation (ECRA) method and framework. In the heterogeneous cloud, there is no centralized resource allocation manager (CRAM) to get all requested resources from a single counter. The proposed methodology for allocating resources divides them according to their cost. The paper’s framework for allocating resources consists of various parts. The Unified Heterogeneous Resource Allocation Manager (UHRAM) part of the framework collects and manages resources from several cloud resource providers. The resource identifier is one of the components in the framework, which is coupled to UHRAM to determine the cost of the resources. The low-cost resources are scheduled and to be in a ready state for allocation. The proposed ECRA is simulated and compared based on parameters like total computation time, response time and resource allocation percentage with existing resource allocation methods. The results prove that the proposed ECRA is efficient in allocating the resources in minimum response time and it allocates maximum resources for lower cost.
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Dolgov, D. A., and E. H. Durfee. "Resource Allocation Among Agents with MDP-Induced Preferences." Journal of Artificial Intelligence Research 27 (December 26, 2006): 505–49. http://dx.doi.org/10.1613/jair.2102.

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Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of self-interested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resource-allocation technique would require the agents to enumerate up to 2^100 resource bundles and the auctioneer to solve an NP-complete problem with an input of that size.
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Gaurav, Raj1 Ankit Nischal2. "Efficient Resource Allocation in Resource provisioning policies over Resource Cloud Communication Paradigm." International Journal on Cloud Computing: Services and Architecture(IJCCSA) 2, June (2018): 01–08. https://doi.org/10.5281/zenodo.1438579.

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Optimal resource utilization for executing tasks within the cloud is one of the biggest challenges. In executing the task over a cloud, the resource provisioner is responsible for providing the resources to create virtual machines. To utilize the resources optimally, the resource provisioner has to take care of the process of allocating resources to Virtual Machine Manager (VMM). In this paper, an efficient way to utilize the resources, within the cloud, to create virtual machines has been proposed considering optimum cost based on performance factor. This performance factor depends upon the overall cost of the resource, communication channel cost, reliability and popularity factor. We have proposed a framework for communication between resource owner and cloud using Resource Cloud Communication Paradigm (RCCP). We extend the CloudSim[2] adding provisioner policies and Efficient Resource Allocation (ERA) algorithm in VMM allocation policy as a decision support for resource provisioner.
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Zheng, Junjun. "Optimization of Resource Allocation of University Innovation and Entrepreneurship Education Based on Collaborative Filtering Algorithm." Journal of Electrical Systems 20, no. 3s (2024): 1853–62. http://dx.doi.org/10.52783/jes.1724.

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Entrepreneurship education resource allocation involves the strategic distribution of resources to support programs and initiatives aimed at fostering entrepreneurial skills and mindset among students. These resources can include funding, faculty support, curriculum development, mentorship opportunities, and access to networks and facilities. Effective resource allocation ensures that entrepreneurship education programs are adequately equipped to provide students with the knowledge, skills, and support needed to succeed as entrepreneurs. By prioritizing resource allocation to areas such as experiential learning, incubation spaces, and networking events, institutions can create a vibrant ecosystem that nurtures innovation and encourages entrepreneurial ventures. This paper presents an innovative approach to optimizing the resource allocation of university innovation and entrepreneurship education through the application of a collaborative filtering algorithm, enhanced by Flemingo Optimized Collaborative Filtering Classification (FOCFC). The study aims to address the challenge of efficiently allocating resources such as funding, mentorship, and infrastructure to support innovation and entrepreneurship initiatives within universities. Through simulated experiments and empirical validations, the effectiveness of the FOCFC-enhanced collaborative filtering algorithm is evaluated in recommending resource allocations tailored to the unique needs and preferences of students and entrepreneurial ventures. Results demonstrate significant improvements in accuracy and efficiency compared to traditional methods, with the FOCFC model achieving a precision rate of 95% in recommending resource allocations. Additionally, the model provides valuable insights into emerging trends and opportunities in the innovation and entrepreneurship ecosystem, enabling universities to adapt their resource allocation strategies proactively. These findings highlight the potential of collaborative filtering algorithms with FOCFC in optimizing resource allocation for university innovation and entrepreneurship education, fostering a supportive and conducive environment for entrepreneurial success.
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Wang, Yanyan, and Baiqing Sun. "A Multiobjective Allocation Model for Emergency Resources That Balance Efficiency and Fairness." Mathematical Problems in Engineering 2018 (October 14, 2018): 1–8. http://dx.doi.org/10.1155/2018/7943498.

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Efficiency and fairness are two important goals of disaster rescue. However, the existing models usually unilaterally consider the efficiency or fairness of resource allocation. Based on this, a multiobjective emergency resource allocation model that can balance efficiency and fairness is proposed. The object of the proposed model is to minimize the total allocating costs of resources and the total losses caused by insufficient resources. Then the particle swarm optimization is applied to solve the model. Finally, a computational example is conducted based on the emergency relief resource allocation after Ya’an earthquake in China to verify the applicability of the proposed model.
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Ma, Ding, M. Onderwater, F. Wetzels, et al. "Cost-Efficient Allocation of Additional Resources for the Service Placement Problem in Next-Generation Internet." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/517409.

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One of the major challenges in next-generation Internet is to allocate services to nodes in the network. This problem, known as theservice placement problem, can be solved by layered graph approach. However, due to the existence of resource bottleneck, the requests are rejected from the beginning in the resource constrained network. In this paper we propose two iterative algorithms for efficient allocation of additional resources in order to improve the ratio of accepted service placement requests. To this end, we (1) introduce a new concept of sensitivity for each service node to locate the bottleneck node, (2) state the problem of allocating additional resources, and (3) use sensitivity to propose a simple iterative algorithm and an utilization-based iterative algorithm for efficient resource allocation. The performance of these two algorithms is evaluated by simulation experiments in a variety of parameter settings. The results show that the proposed algorithms increase request acceptance ratio significantly by allocating additional resources into the bottleneck node and links. The utilization-based iterative algorithm also decreases the long-term cost by making efficient use of additional resources.
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M, Sumathi, Niranjana B, Akshaya C, Ajitha M, and Bhavadharanee M. "Round Robin Based Efficient Resource Allocation and Utilization in an Organization." International Research Journal of Multidisciplinary Technovation 2, no. 2 (2020): 16–22. http://dx.doi.org/10.34256/irjmt2023.

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In an organization, resource allocation to a request is a complex task. Traditionally, resource allocation is done through manually with high time consumption. Similarly, collision is occurring for allocating a single resource to multiple requests. Thus, leads to complex problems and slow-down the working process. The existing resource allocation technique, allocate resources continuously to a specific request and omit another request. This kind of allocation technique also leads to lots of critical issues. That is the non-allocated process never gets a resource. To overcome these issues, the Round Robin based Resource allocation and Utilization technique is proposed in this work. The Round Robin technique allocates resources to the request in an efficient with equal priority. Similarly, the proposed technique reduces collision and takes less time for mapping a resource with a request. The experimental results shows improved accuracy than the traditional resource allocation technique.
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Chen, Chao, Changjun Fan, and Xingxing Liang. "A Multiobjective Resource Allocation Algorithm for Robust Project Scheduling." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 7701–4. http://dx.doi.org/10.1166/jctn.2016.4426.

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Resource allocation is an important procedure which involves allocating finite resources to the activities of a given baseline schedule. Based on the conception of Pareto Optimization, a multiobjective optimization approach for the resource allocation problem is proposed in this paper. The problem is first described. Then the detailed procedure of the proposed algorithm is given. Finally, an extensive computational results obtained on a set of benchmark problems are reported.
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Deng, Hongyu, Cheng Wu, and Yiming Wang. "A cognitive gateway-based spectrum sharing method in downlink round robin scheduling of LTE system." Modern Physics Letters B 31, no. 19-21 (2017): 1740070. http://dx.doi.org/10.1142/s021798491740070x.

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A key technique of LTE is how to allocate efficiently the resource of radio spectrum. Traditional Round Robin (RR) scheduling scheme may lead to too many resource residues when allocating resources. When the number of users in the current transmission time interval (TTI) is not the greatest common divisor of resource block groups (RBGs), and such a phenomenon lasts for a long time, the spectrum utilization would be greatly decreased. In this paper, a novel spectrum allocation scheme of cognitive gateway (CG) was proposed, in which the LTE spectrum utilization and CG’s throughput were greatly increased by allocating idle resource blocks in the shared TTI in LTE system to CG. Our simulation results show that the spectrum resource sharing method can improve LTE spectral utilization and increase the CG’s throughput as well as network use time.
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Dissertations / Theses on the topic "Resource allocating"

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Ramanan, Amirthalingam. "Designing a resource-allocating codebook for patch-based visual object recognition." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/159175/.

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The state-of-the-art approach in visual object recognition is the use of local information extracted at several points or image patches from an image. Local information at specific points can deal with object shape variability and partial occlusions. The underlying idea is that, in different images, the statistical distribution of the patches is different, which can be effectively exploited for recognition. In such a patch-based object recognition system, the key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook plays a central role that affects the model’s complexity. The construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. This thesis demonstrates a novel approach, that we call resource-allocating codebook (RAC), to constructing a discriminant codebook in a one-pass design procedure inspired by the resource-allocation network family of algorithms. The RAC approach slightly outperforms more traditional approaches due to its tendency to spread out the cluster centres over a broader range of the feature space thereby including rare low-level features in the codebook than density-preserving clustering-based codebooks. Our algorithm achieves this performance at drastically reduced computing times, because apart from an initial scan through a small subset to determine length scales, each data item is processed only once. We illustrate some properties of our method and compare it to a closely related approach known as the mean-shift clustering technique. A pruning strategy has been employed to tackle a few outliers when assigning each feature in images to the closest codeword to create a histogram representation for each image. Features whose distance from the closest codeword exceeds an empirical distance maximum are neglected. A recognition system that learns incrementally with training images and the output classifier accounting for class-specific discriminant features is also presented. Furthermore, we address an approach which, instead of clustering, adaptively constructs a codebook by computing Fisher scores between the classes of interest. This thesis also demonstrates a novel sequential hierarchical clustering technique that initially builds a hierarchical tree from a small subset of the data, while the remaining data are processed sequentially and the tree adapted constructively. Evaluations performed with this approach show that the performance is comparable while reducing the computational needs. Finally, during the process of classification, we demonstrate a new learning architecture for multi-class classification tasks using support vector machines. This technique is faster in testing compared to directed acyclic graph (DAG) SVMs, while maintaining comparable performance to the standard multi-class classification techniques.
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Ahlén, Niclas. "ARAVQ som datareducerare för en klassificeringsuppgift inom datautvinning." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-928.

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<p>Adaptive Resource Allocating Vector Quantizer (ARAVQ) är en teknik för datareducering för mobila robotar. Tekniken har visats framgångsrik i enkla miljöer och det har spekulerats i att den kan fungera som ett generellt datautvinningsverktyg för tidsserier. I rapporten presenteras experiment där ARAVQ används som datareducerare på en artificiell respektive en fysiologisk datamängd inom en datautvinningskontext. Dessa datamängder skiljer sig från tidigare robotikmiljöer i och med att de beskriver objekt med diffusa eller överlappande gränser i indatarymden. Varje datamängd klassificeras efter datareduceringen med hjälp av artificiella neuronnät. Resultatet från experimenten tyder på att klassificering med ARAVQ som datareducerare uppnår ett betydligt lägre resultat än om ARAVQ inte används som datareducerare. Detta antas delvis bero på den låga generaliserbarheten hos de lösningar som skapas av ARAVQ. I diskussionen föreslås att ARAVQ skall kompletteras med en funktion för grannskap, motsvarande den som finns i Self-Organizing Map. Med ett grannskap behålls relationerna mellan de kluster som ARAVQ skapar, vilket antas minska följderna av att en beskrivning hamnar i ett grannkluster</p>
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Kroshl, William M. "Allocation of Resources to Defend Spatially Distributed Networks Using Game Theoretic Allocations." Thesis, The George Washington University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3669710.

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<p> This dissertation presents research that focuses on efficient allocation of defense resources to minimize the damage inflicted on a spatially distributed physical network such as a pipeline, water system, or power distribution system from an attack by an active adversary. The allocation methodology recognizes the fundamental difference between preparing for natural disasters such as hurricanes, earthquakes, or even accidental systems failures and the problem of allocating resources to defend against an opponent who is aware of and anticipating the defender's efforts to mitigate the threat. </p><p> Conceptualizing the problem as a Stackelberg "leader-follower" game, the defender first places his assets to defend key areas of the network, and the attacker then seeks to inflict the maximum damage possible within the constraints of resources and network structure. The approach is to utilize a combination of integer programming and agent-based modeling to allocate the defensive resources. The criticality of arcs in the network is estimated by a deterministic network interdiction formulation, a maximum-flow linear program (LP), or a combination of both of these methods, which then inform an evolutionary agent-based simulation. The evolutionary agent-based simulation is used to determine the allocation of resources for attackers and defenders that results in evolutionarily stable strategies in which actions by either side alone cannot increase their share of victories. </p><p> These techniques are demonstrated on several example networks using several different methods of evaluating the value of the nodes and comparing the evolutionary agent-based results to a more traditional, Probabilistic Risk Analysis (PRA) approach. The results show that the agent-based allocation approach results in a greater percentage of defender victories than does the PRA-based allocation approach.</p>
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Teslyuk, Nataliya. "Natural resources: a curse or a blessing? Political incentives in the resource allocation (example of gazprom)." Thesis, Видавництво СумДУ, 2007. http://essuir.sumdu.edu.ua/handle/123456789/13105.

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In my presentation I will reflect on whether politicians (in particular Russian ones) tend to over-extract the natural resources, because of the boundaries to their short-term power and the willingness to satisfy the needs of the population during the period when they are in control; this strategy can contradict effective path in the medium, and long run. There is a model developed to answer to this questions by the scientists of Norwegian University of Science and Technology, James A. Robinson, Ragnar Torvik, and Thierry Verdier which can be applied to Russia as well, but interestingly there are major differences, and it seems to be that Russia’s situation is distinct, what does not surprise much taking into account its position on the market. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/13105
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AuYoung, Alvin. "Practical market-based resource allocation." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3397175.

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Thesis (Ph. D.)--University of California, San Diego, 2010.<br>Title from first page of PDF file (viewed March 29, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 146-155).
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Choueiry, Berthe Y. Choueiry Berthe Yazid. "Abstraction methods for resource allocation /." [S.l.] : [s.n.], 1994. http://library.epfl.ch/theses/?nr=1292.

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Muñoz, i. Solà Víctor. "Robustness on resource allocation problems." Doctoral thesis, Universitat de Girona, 2011. http://hdl.handle.net/10803/7753.

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En problemes d'assignació de recursos, normalment s'han de tenir en compte les incerteses que poden provocar canvis en les dades inicials. Aquests canvis dificulten l'aplicabilitat de les planificacions que s'hagin fet inicialment.<br/>Aquesta tesi se centra en l'elaboració de tècniques que consideren la incertesa alhora de cercar solucions robustes, és a dir solucions que puguin continuar essent vàlides encara que hi hagi canvis en l'entorn. Particularment, introduïm el concepte de robustesa basat en reparabilitat, on una solució robusta és una que pot ser reparada fàcilment en cas que hi hagi incidències. La nostra aproximació es basa en lògica proposicional, codificant el problema en una fórmula de satisfactibilitat Booleana, i aplicant tècniques de reformulació per a la generació de solucions robustes. També presentem un mecanisme per a incorporar flexibilitat a les solucions robustes, de manera que es pugui establir fàcilment el grau desitjat entre robustesa i optimalitat de les solucions.<br>Resource allocation problems usually include uncertainties that can produce changes in the data of the problem. These changes may cause difficulties in the applicability of the solutions.<br/>This thesis is focused in the elaboration of techniques that take into account such uncertainties while searching for robust solutions, i.e. solutions that can remain valid even if there are changes in the environment. Particularly, we introduce the concept of robustness based on reparability, where a robust solution is one that can be easily repaired when unexpected events occur. Our approach is based in propositional logic, encoding the problem to a Boolean formula, and applying reformulation techniques in order to generate robust solutions. Additionally, we present a mechanism to incorporate flexibility to the robust solutions, so that one can easily set the desired degree between optimality and robustness.
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Tureli, Didem Kivanc. "Resource allocation for multicarrier communications /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6068.

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Lai, John Kwang. "Truthful and Fair Resource Allocation." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10928.

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How should we divide a good or set of goods among a set of agents? There are various constraints that we can consider. We consider two particular constraints. The first is fairness - how can we find fair allocations? The second is truthfulness - what if we do not know agents valuations for the goods being allocated? What if these valuations need to be elicited, and agents will misreport their valuations if it is beneficial? Can we design procedures that elicit agents' true valuations while preserving the quality of the allocation? We consider truthful and fair resource allocation procedures through a computational lens. We first study fair division of a heterogeneous, divisible good, colloquially known as the cake cutting problem. We depart from the existing literature and assume that agents have restricted valuations that can be succinctly communicated. We consider the problems of welfare-maximization, expressiveness, and truthfulness in cake cutting under this model. In the second part of this dissertation we consider truthfulness in settings where payments can be used to incentivize agents to truthfully reveal their private information. A mechanism asks agents to report their private preference information and computes an allocation and payments based on these reports. The mechanism design problem is to find incentive compatible mechanisms which incentivize agents to truthfully reveal their private information and simultaneously compute allocations with desirable properties. The traditional approach to mechanism design specifies mechanisms by hand and proves that certain desirable properties are satisfied. This limits the design space to mechanisms that can be written down and analyzed. We take a computational approach, giving computational procedures that produce mechanisms with desirable properties. Our first contribution designs a procedure that modifies heuristic branch and bound search and makes it usable as the allocation algorithm in an incentive compatible mechanism. Our second contribution draws a novel connection between incentive compatible mechanisms and machine learning. We use this connection to learn payment rules to pair with provided allocation rules. Our payment rules are not exactly incentive compatibility, but they minimize a measure of how much agents can gain by misreporting.<br>Engineering and Applied Sciences
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Reid, Jane Margaret. "Resource allocation during avian incubation." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392460.

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Books on the topic "Resource allocating"

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Saarinen, Phyllis Park. Allocating water under scarcity conditions in Florida: Evaluation and prospectus. Food and Resource Economics Dept., Florida Agricultural Experiment Stations, Institute of Food and Agricultural Sciences, University of Florida, 1993.

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Estelle, Gilman, and University of Birmingham. Institute of Public and Environmental Health., eds. Resource allocation & health needs: From research to policy : proceedings of a conference held to consider assessment of health care needs in relation to framing policies for allocating resources in the Health Service. H.M.S.O., 1994.

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Steenburgh, Thomas. Allocating marketing resources. Harvard Business School, 2008.

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Chambers, Jay G. Measuring resources in education: From accounting to the resource cost model approach. U.S. Dept. of Education, Office of Educational Research and Improvement, National Center for Education Statistics, 1999.

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Humber, James M., and Robert F. Almeder, eds. Allocating Health Care Resources. Humana Press, 1995. http://dx.doi.org/10.1007/978-1-59259-447-4.

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Santos, Brian L. Dos. Allocating information systems resources. Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1990.

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Ushakov, Igor A., ed. Optimal Resource Allocation. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118400715.

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Luss, Hanan. Equitable Resource Allocation. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118449189.

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Langholtz, Harvey J., Antoinette T. Marty, Christopher T. Ball, and Eric C. Nolan. Resource-Allocation Behavior. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1131-1.

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1948-, Langholtz Harvey J., ed. Resource-allocation behavior. Kluwer Academic Publishers, 2003.

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Book chapters on the topic "Resource allocating"

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Livenson, Ilja, Ahti Saar, Marina Adomeit, et al. "Federated Resource Allocation for HPC Services." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86240-3_9.

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Abstract This paper presents an implementation of a federated resource allocation system (Puhuri), which offers a full set of functions from the application phase for computing infrastructure resources to consumption reporting. The implementation is based on the Puhuri project of Nordic e-Infrastructure Collaboration (NeIC) [1]. Shared research infrastructures, particularly high-performance computers, necessitate a robust mechanism for allocating resources in accordance with predefined policies. An allocation serves as authorisation to utilise a system in an agreed-upon manner. The impact of Puhuri on shared research infrastructures is to enable multiple resource allocation teams to allocate resources across different domains and organisations. Puhuri has integrated the LUMI EuroHPC supercomputer [2] and the biodiversity digital twin workflow management framework Lexis. Additionally, Puhuri has also been collecting requirements and doing technical integrations in a wider scope. Users accessing the system must be unequivocally identified. This process, known as authentication, ensures that the right individuals utilise the allocated resources to which they are authorised by the resource allocation team. GÉANT has established the MyAccessID service [3] for federated authentication, which Puhuri also utilises. One of the challenges is related to the user identity vetting for the users whose identity provider is not signalling that they are verifying users’ identities in the required manner, which requires them to use commercial third-party services for automatic identity vetting based on user’s identity cards.
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Kirk, David B., Jay K. Strosnider, and John E. Sasinowski. "Allocating Smart Cache Segments for Schedulability." In Foundations of Real-Time Computing: Scheduling and Resource Management. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3956-8_9.

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Vasilecas, Olegas, Kestutis Normantas, Toma Rusinaite, Titas Savickas, and Tadas Vysockis. "Shared Resource Model for Allocating Resources to Activities in BP Simulation." In Communications in Computer and Information Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46254-7_18.

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Tabuchi, Toshihisa, Seiichi Ozawa, and Asim Roy. "An Autonomous Learning Algorithm of Resource Allocating Network." In Intelligent Data Engineering and Automated Learning - IDEAL 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04394-9_17.

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Linåker, Fredrik, and Lars Niklasson. "Sensory Flow Segmentation Using a Resource Allocating Vector Quantizer." In Advances in Pattern Recognition. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44522-6_88.

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Pertselakis, Minas, Natali Raouzaiou, and Andreas Stafylopatis. "An Adaptive Resource Allocating Neuro-Fuzzy Inference System with Sensitivity Analysis Resource Control." In IFIP Advances in Information and Communication Technology. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0221-4_59.

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Gebrealif, Yodit, Mohammed Mubarkoot, Jörn Altmann, and Bernhard Egger. "Architecture for Orchestrating Containers in Cloud Federations." In Economics of Grids, Clouds, Systems, and Services. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92916-9_6.

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AbstractContainerization technology helps achieving not only better portability and interoperability but also better performance and efficiency on various cloud computing arrangements. Such technology is expected to empower cloud federations by enhancing portability and scalability across the federation. In this paper, we propose an architecture by adding two subcomponents to the NIST reference architecture for identifying resources and managing container orchestration in cloud federation environments. The architecture adds two subcomponents to the NIST reference architecture. The proposed two new sub-components enable resource identification and container orchestration across cloud federation members. These names of the two subcomponents are the Resource Identifier and the Container Orchestrator, respectively. The Resource Identifier component identifies the appropriate federated member for allocating tasks based on previous experience and current status. The Container Orchestrator facilitates the management and orchestration of containers at the federation level. We also identified several techniques, which can be used for resource identification. Among those, linear regression technique is selected for resource provisioning and identification of federation members. Further, these techniques are also expected to learn from log files from previous executions and prioritize resources based on the current resource status and previous experience.
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Ciancia, Vincenzo, and Matteo Sammartino. "A Class of Automata for the Verification of Infinite, Resource-Allocating Behaviours." In Trustworthy Global Computing. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45917-1_7.

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Ribeiro, Bernardete, Ricardo Quintas, and Noel Lopes. "Evaluation of a Resource Allocating Network with Long Term Memory Using GPU." In Adaptive and Natural Computing Algorithms. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20267-4_5.

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Ozawa, Seiichi, and Keisuke Okamoto. "An Incremental Learning Algorithm for Resource Allocating Networks Based on Local Linear Regression." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_64.

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Conference papers on the topic "Resource allocating"

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Lee, Dongjoon, Jongin Choe, Chanyoung Park, Kyungtae Kang, Mahmut Kandemir, and Wonil Choi. "An Autonomic Resource Allocating SSD." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546802.

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Kupcs, Raimonds, and Arnis Lenerts. "REGULATORY FRAMEWORK OF RESOURCE MANAGEMENT IN EDUCATION SYSTEM: A COMPREHENSIVE ANALYSIS." In 11th SWS International Scientific Conferences on SOCIAL SCIENCES - ISCSS 2024. SGEM WORLD SCIENCE, 2024. https://doi.org/10.35603/sws.iscss.2024/s02/15.

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The constant need to ensure the efficient use of resources, especially in the period of a challenging geopolitical situation, when the mobilization of resources in every sector of the national economy is of decisive importance, while not allowing the quality of educational services to decrease. The regulatory framework governing resource management within education system of Latvia is a multifaceted construct crucial for ensuring efficient allocation and utilization of resources. This paper provides an in-depth examination of the overall regulatory framework, emphasizing its intricate layers and mechanisms. However, it acknowledges a limitation concerning the scope of educational institutions under scrutiny. Thus, paper outlines classification of resources within educational institutions, encompassing various categories and, moreover, investigates the intricate relationship between the regulatory framework and different types of resources. It is an attempt to find and create a connection between the resources of educational institutions and the regulatory framework of the educational system for further research aimed at advancing the development of resource management model. As a result of the analysis, paper provides an insight how the regulatory policy affects the distribution and use of resources in educational institutions and where resource management strategy should focus, seeing the main directions in their optimization in order to improve the overall educational results and the efficiency of the institutions
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Richards, Robert A. "Reduced Project Duration via Intelligent Scheduling for Ship Production/Repair." In SNAME Maritime Convention. SNAME, 2022. http://dx.doi.org/10.5957/smc-2022-055.

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Ship and submarine production and repair have benefited significantly from more intelligently allocating resources and managing other constraints, thereby increasing efficiency, and reducing overall project duration. In complex production and repair environments, such as ship production/repair, the method of allocating resources and managing other constraints significantly affects the efficiency of progress and thus the overall project duration. Resources include human resources, equipment resources and physical-space resources. Due to the inherent complexity of resource allocation and constraint management for such complex environments, the project durations can be 25%+ longer than necessary. This paper shares the real-world results experienced by the authors as well as similar results found in the literature showing the major difference in project duration due to resource-scheduling techniques.
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Kyropoulou, Maria, Warut Suksompong, and Alexandros A. Voudouris. "Almost Envy-Freeness in Group Resource Allocation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/57.

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We study the problem of fairly allocating indivisible goods between groups of agents using the recently introduced relaxations of envy-freeness. We consider the existence of fair allocations under different assumptions on the valuations of the agents. In particular, our results cover cases of arbitrary monotonic, responsive, and additive valuations, while for the case of binary valuations we fully characterize the cardinalities of two groups of agents for which a fair allocation can be guaranteed with respect to both envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX). Moreover, we introduce a new model where the agents are not partitioned into groups in advance, but instead the partition can be chosen in conjunction with the allocation of the goods. In this model, we show that for agents with arbitrary monotonic valuations, there is always a partition of the agents into two groups of any given sizes along with an EF1 allocation of the goods. We also provide an extension of this result to any number of groups.
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Bott, Terry F., and Stephen W. Eisenhawer. "A Structured Approach to Resource Allocation." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-2998.

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Allocating limited resources among competing candidates is an important problem in management. In this paper, we describe a structured and flexible approach to resource allocation using logic-evolved decision (LED) analysis. LED analysis uses logic models to generate an exhaustive set of competing alternatives and the inferential model that is used for preference ordering of these alternatives. The inferential models can use data in numerical, linguistic, or mixed forms; uncertainty in the evaluation results can be expressed using probabilistic- or linguistic-based methods. We illustrate the use of LED analysis for an allocation problem with numerical input data and for an allocation problem with only linguistic input data.
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Kumar, Praveen, Priyavrat Singh Yadav, Kashish Bhutani, Nagma Arora, Deepanshu Jain, and Bhawesh Dabas. "Allocating resource dynamically in cloud computing." In 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). IEEE, 2017. http://dx.doi.org/10.1109/ictus.2017.8286014.

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Richards, Robert A. "Reduced Project Duration & Improved Critical Resource Determination via Intelligent Scheduling: Navy and Other Applications." In SNAME Maritime Convention. SNAME, 2012. http://dx.doi.org/10.5957/smc-2012-p40.

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In complex production and maintenance environments, such as ship production, the method of allocating resources and managing other constraints significantly affects the efficiency of progress as well as the overall project duration. Resources include human resources, equipment resources and physical-space resources. Due to the inherent complexity of resource allocation and constraint management for such complex production and maintenance environments, the project durations can be two-times, or more, longer than needed. Unfortunately, most commercial project management software does not benefit from such intelligent scheduling technology. Software that does not leverage intelligent scheduling may also determine incorrect critical resources. A resource is critical if the project duration would be shorter if more of that resource were available. So not using intelligent scheduling will result in longer than necessary schedules and might direct users in the wrong direction per critical resources, thus leading to wasteful acquisition of more resources to shorten the schedule when this is an option.
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Yuen, Sheung Man, and Warut Suksompong. "Approximate Envy-Freeness in Graphical Cake Cutting." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/326.

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We study the problem of fairly allocating a divisible resource in the form of a graph, also known as graphical cake cutting. Unlike for the canonical interval cake, a connected envy-free allocation is not guaranteed to exist for a graphical cake. We focus on the existence and computation of connected allocations with low envy. For general graphs, we show that there is always a 1/2-additive-envy-free allocation and, if the agents' valuations are identical, a (2+\epsilon)-multiplicative-envy-free allocation for any \epsilon &gt; 0. In the case of star graphs, we obtain a multiplicative factor of 3+\epsilon for arbitrary valuations and 2 for identical valuations. We also derive guarantees when each agent can receive more than one connected piece. All of our results come with efficient algorithms for computing the respective allocations.
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Nag, Arindam, and Joydeep Ghosh. "Flexible resource-allocating network for noisy data." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers, David B. Fogel, James C. Bezdek, and Bruno Bosacchi. SPIE, 1998. http://dx.doi.org/10.1117/12.304846.

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Chan, Hau, Long Tran-Thanh, and Vignesh Viswanathan. "Fighting Wildfires under Uncertainty - A Sequential Resource Allocation Approach." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/596.

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Standard disaster response involves using drones (or helicopters) for reconnaissance and using people on the ground to mitigate the damage. In this paper, we look at the problem of wildfires and propose an efficient resource allocation strategy to cope with both dynamically changing environment and uncertainty. In particular, we propose Firefly, a new resource allocation algorithm, that can provably achieve optimal or near optimal solutions with high probability by first efficiently allocating observation drones to collect information to reduce uncertainty, and then allocate the firefighting units to extinguish fire. For the former, Firefly uses a combination of maximum set coverage formulation and a novel utility estimation technique, and it uses a knapsack formulation to calculate the allocation for the latter. We also demonstrate empirically by using a real-world dataset that Firefly achieves up to 80-90% performance of the offline optimal solution, even with a small amount of drones, in most of the cases.
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Reports on the topic "Resource allocating"

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Hornig, Constance, and Sandra Cointreau. Global Review of Economic Instruments for Solid Waste Management in Latin America. Inter-American Development Bank, 2003. http://dx.doi.org/10.18235/0006681.

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There is a strong relationship between economic instruments and the polluter-pays principle. That principle involves allocating costs of waste management services, resource consumption, and pollution control to consumers and producers. Inherent in this principle, the polluter bears all internal and external environmental costs, with the goal that all subsidies for resource use, production and waste management services are eliminated.
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Desai, Jairaj, Jijo K. Mathew, Woosung Kim, et al. Dashboards for Real-time Monitoring of Winter Operations Activities and After-action Assessment. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317252.

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The Indiana Department of Transportation (INDOT) operates a fleet of nearly 1100 snowplows and spends up to $60M annually on snow removal and de-icing as part of their winter operation maintenance activities. Systematically allocating resources and optimizing material application rates can potentially save revenue that can be reallocated for other roadway maintenance operations. Modern snowplows are beginning to be equipped with a variety of Mobile Road Weather Information Sensors (MARWIS) which can provide a host of analytical data characterizing on-the-ground conditions during periods of wintry precipitation. Traffic speeds fused with road conditions and precipitation data from weather stations provide a uniquely detailed look at the progression of a winter event and the performance of the fleet. This research uses a combination of traffic speeds, MARWIS and North American Land Data Assimilation System (NLDAS) data to develop real-time dashboards characterizing the impact of precipitation and pavement surface temperature on mobility. Twenty heavy snow events were identified for the state of Indiana from November 2018 through April 2019. Two particular instances, that impacted 182 miles and 231 miles of interstate at their peaks occurred in January and March, respectively, and were used as a case study for this paper. The dashboards proposed in this paper may prove to be particularly useful for agencies in tracking fleet activity through a winter storm, helping in resource allocation and scheduling and forecasting resource needs.
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Elacqua, Gregory, Mateus Rodrigues, and Leonardo Rosa. IImpact of Monetary Incentives on Teacher Decisions to Leave and Choose Schools: Evidence from a Policy Reform in Sao Paulo. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013366.

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Teacher turnover is a major challenge for human resource management in schools, adversely affecting student learning. We examine the impact of a monetary incentive program introduced in 2022 in the city of Sao Paulo, Brazil, which aims to reduce teacher turnover by allocating wage premiums ranging from 5% to 25% of base salary based on schools turnover levels. Our results show a significant reduction in turnover: an average decrease of 18% across all schools, with an even more pronounced 30% reduction in schools offering higher incentives. Notably, the program also attracted new teachers to these higher-incentive schools. An analysis of teacher preferences similarly reveals a shift towards schools offering greater wage premiums. Furthermore, we find that schools offering high incentives experienced significant improvements in student test scores, with gains of 0.3-0.6 standard deviations in standardized assessments. The findings demonstrate the effectiveness of monetary incentives in mitigating teacher turnover and improving educational outcomes, providing evidence-based guidance for policymakers developing teacher retention strategies.
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Jones, P. Resource Allocation, Control, and Accounting for the Use of Network Resources. RFC Editor, 1992. http://dx.doi.org/10.17487/rfc1346.

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Raw, Silvia, and Jose Ignacio Sembler. Approach Paper: Evaluation of the Fund for Special Operations During the Eighth Replenishment (1994-2010): Part II. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0010424.

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OVE has carried out two Fund for Special Operations (FSO) evaluations. The first, "Oversight Note on the Performance Criteria for Allocating Concessional Resources" (RE-279), was presented to the Board of Executive Directors in 2003 and provided a preliminary review of the new performance-related allocation criteria introduced in 2002. In October 2010 OVE presented the "Evaluation of the Fund for Special Operations during the Eighth Replenishment (1994-2010) - Part I" (RE-376), in anticipation of the FSO replenishment through IDB-9. This document focused primarily on the implications of the changing methodologies for allocating FSO resources. The objective of this evaluation is to complete the assessment of the performance of IDB's FSO loan program during the period of the Eighth Replenishment (1994-2010), focusing as stated in RE-376 on financing and results.
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Stokes, Brian J., and Gregory S. Parnell. Alternate Resource Allocation Techniques. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada405460.

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Zilberman, David, and Eithan Hochman. Price Evaluation and Allocation of Water under Alternative Water Rights System - Part II. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7573067.bard.

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This project is a continuation of US 2081-91. Together they develop a conceptual and empirical framework to analyze alternative forms of water reform that lead to efficient pricing of water. Our analysis demonstrates that the transition from water rights systems to water trading may lead to improved resource allocation even when overall availability of water resources declines. We introduce two systems of water trading, passive markets and active markets, and show that passive markets lead to efficient resource allocation with lower transaction costs. We demonstrate that both methods of trading are superior to block pricing. We identify the political economic situations that would lead to each type of water resource allocation. Examples from Israel and California are used to demonstrate the conceptual results.
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Müller, Benito. Performance-based formulaic resource allocation. Oxford Institute for Energy Studies, 2014. http://dx.doi.org/10.26889/9781907555862.

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Hansen, Jeff, Scott Hissam, B. C. Meyers, et al. Resource Allocation in Dynamic Environments. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada609913.

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Keller, William C. The Defense Resource Allocation Process. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada326185.

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